Artificial intelligence to the rescue of the security of financial transactions

Because of its critical importance for the global economy, the world of finance is among the most regulated of areas.  Its institutions have a duty of transparency and security in relation to their clients. The entry into force at the beginning of the year of MiFID II European directive on markets in financial instruments will strengthen the concept of transparency and improve client protection. The European General Data Protection Regulation (GDPR) will for its part strengthen requirements in the area of the security of client data.

In this restrictive regulatory context which combines with growing pressure on prices and more global competition, financial institutions must permanently innovate technically and offer new products while ensuring the security of transactions.

Understanding and measuring the growing risks which a financial institution faces today.

First of all, the operational risk. This includes the risk of direct or indirect losses due to an inadequacy or failure of the institution’s procedures, personnel or internal systems, or to external risks (flooding, fire and so on). The counterparty or risk of default of different counterparties, highlighted by the bankruptcy of Lehman Brothers (2008), is now taken more into account both by the financial institutions and the regulator, through the strengthening of its applicable regulatory framework (Basle III, EMI, and so on). Financial investments which are difficult to liquidate rapidly are also a major risk for the financial sector, notably for investors who have taken a significant liquidity risk which could lead to large-scale capital losses. The financial sector remains a major actor in the fight against laundering. It has since the advent of digital technology been under the growing threat of cyber risk. Fraudsters are not lacking in ingenuity in the area of cyberattacks aimed at the world of finance (botnet, ransomware, DDoS attacks, and so on).

Today, to guard against these risks, institutions are forced to invest continually to ensure the security of their transactions, which has a de facto impact on their operational costs. On the market, security solutions exist at both global (with a network approach) and specific (for example, to exclusively combat money laundering) levels to allow them to guarantee the level of security required by the legislation.

What alternative to the ingenuity of risk?

While fraudsters use the new technologies to target actors in the world of finance, the latter are also accelerating their technological turn. Little by little, they are turning to solutions based on a layer of Artificial Intelligence (AI).

In the financial domain, AI allows exploitation of historic data in institutions to ensure an additional security for financial transactions. This layer of intelligence has the advantage of facilitating the analysis of the behaviour of the investor and of partners, as well as the nature and characteristics of the transactions themselves. Artificial Intelligence allows systems to self-learn - the notion of machine learning – but also to detect and warn of any abnormal behaviour (unusual beneficiaries, payment in countries under surveillance, repetition of amounts and so on). The integration of Artificial Intelligence into financial management solutions offers a supplementary guarantee of security control.

Financial institutions have begun to explore the path of the predictive potential offered by AI. The next two years will see significant advances facilitating the massive adoption of solutions derived from Artificial Intelligence. Indeed, in an industry increasingly torn by regulatory requirements in the area of transparency and security, Artificial Intelligence represents a real advantage in the area of detection of suspect behaviour from control panels. And that is not even counting the possibilities offered in the area of predictive maintenance, and control of associated costs.


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